SOTAVerified

Image Super-Resolution

Image Super-Resolution is a machine learning task where the goal is to increase the resolution of an image, often by a factor of 4x or more, while maintaining its content and details as much as possible. The end result is a high-resolution version of the original image. This task can be used for various applications such as improving image quality, enhancing visual detail, and increasing the accuracy of computer vision algorithms.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 651–675 of 1589 papers

TitleStatusHype
CurvPnP: Plug-and-play Blind Image Restoration with Deep Curvature DenoiserCode0
A Comprehensive Survey of Transformers for Computer Vision—0
Contrastive Learning for Climate Model Bias Correction and Super-Resolution—0
The Best of Both Worlds: a Framework for Combining Degradation Prediction with High Performance Super-Resolution NetworksCode1
RRSR:Reciprocal Reference-based Image Super-Resolution with Progressive Feature Alignment and Selection—0
Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 challenge: ReportCode1
Underwater Image Super-Resolution using Generative Adversarial Network-based Model—0
Measurement-Consistent Networks via a Deep Implicit Layer for Solving Inverse Problems—0
Mixture-Net: Low-Rank Deep Image Prior Inspired by Mixture Models for Spectral Image Recovery—0
HyperSound: Generating Implicit Neural Representations of Audio Signals with Hypernetworks—0
Fine-tuned Generative Adversarial Network-based Model for Medical Image Super-Resolution—0
Combining Attention Module and Pixel Shuffle for License Plate Super-ResolutionCode1
Iris super-resolution using CNNs: is photo-realism important to iris recognition?—0
Single Image Super-Resolution via a Dual Interactive Implicit Neural NetworkCode1
How Real is Real: Evaluating the Robustness of Real-World Super Resolution—0
Boomerang: Local sampling on image manifolds using diffusion models—0
Single Image Super-Resolution Using Lightweight Networks Based on Swin Transformer—0
Real Image Super-Resolution using GAN through modeling of LR and HR process—0
ITSRN++: Stronger and Better Implicit Transformer Network for Continuous Screen Content Image Super-Resolution—0
ISTA-Inspired Network for Image Super-Resolution—0
CUF: Continuous Upsampling Filters—0
Scene Text Image Super-Resolution via Content Perceptual Loss and Criss-Cross Transformer Blocks—0
Efficient Image Super-Resolution using Vast-Receptive-Field AttentionCode1
Deep Fourier Up-SamplingCode0
DA-VSR: Domain Adaptable Volumetric Super-Resolution For Medical Images—0
Show:102550
← PrevPage 27 of 64Next →
All datasetsSet14 - 4x upscalingBSD100 - 4x upscalingUrban100 - 4x upscalingManga109 - 4x upscalingSet5 - 2x upscalingSet14 - 2x upscalingSet5 - 3x upscalingBSD100 - 2x upscalingUrban100 - 2x upscalingSet14 - 3x upscalingUrban100 - 3x upscalingBSD100 - 3x upscaling

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DRCT-LPSNR29.54—Unverified
2HMA†PSNR29.51—Unverified
3Hi-IR-LPSNR29.49—Unverified
4HAT-LPSNR29.47—Unverified
5HAT_FIRPSNR29.44—Unverified
6DRCTPSNR29.4—Unverified
7HATPSNR29.38—Unverified
8CPAT+PSNR29.36—Unverified
9SwinFIRPSNR29.36—Unverified
10CPATPSNR29.34—Unverified
#ModelMetricClaimedVerifiedStatus
1DRCT-LPSNR28.16—Unverified
2HMA†PSNR28.13—Unverified
3Hi-IR-LPSNR28.13—Unverified
4HAT-LPSNR28.09—Unverified
5HAT_FIRPSNR28.07—Unverified
6CPAT+PSNR28.06—Unverified
7DRCTPSNR28.06—Unverified
8HATPSNR28.05—Unverified
9CPATPSNR28.04—Unverified
10SwinFIRPSNR28.03—Unverified
#ModelMetricClaimedVerifiedStatus
1Hi-IR-LPSNR28.72—Unverified
2DRCT-LPSNR28.7—Unverified
3HMA†PSNR28.69—Unverified
4HAT-LPSNR28.6—Unverified
5HAT_FIRPSNR28.43—Unverified
6DRCTPSNR28.4—Unverified
7HATPSNR28.37—Unverified
8CPAT+PSNR28.33—Unverified
9CPATPSNR28.22—Unverified
10PFTPSNR28.2—Unverified